A. David, J. Barmherzig, . Sun, J. Emmanuel, . Candes et al., Holographic phase retrieval and optimal reference design, 2019.

A. Beck, P. Stoica, and J. Li, Exact and approximate solutions of source localization problems, IEEE Transactions on Signal Processing, vol.56, issue.5, pp.1770-1778, 2008.

R. Beinert, One-dimensional phase retrieval with additional interference intensity measurements, Results in Mathematics, vol.72, issue.1-2, pp.1-24, 2017.

J. Emmanuel, X. Candes, M. Li, and . Soltanolkotabi, Phase retrieval via wirtinger flow: Theory and algorithms, IEEE Transactions on Information Theory, vol.61, issue.4, pp.1985-2007, 2015.

I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, Euclidean distance matrices: essential theory, algorithms, and applications, IEEE Signal Processing Magazine, vol.32, issue.6, pp.12-30, 2015.

A. Drémeau, A. Liutkus, D. Martina, O. Katz, C. Schülke et al., Reference-less measurement of the transmission matrix of a highly scattering material using a dmd and phase retrieval techniques, Optics express, vol.23, issue.9, pp.11898-11911, 2015.

. James-r-fienup, Phase retrieval algorithms: a comparison, Applied optics, vol.21, issue.15, pp.2758-2769, 1982.

N. Halko, J. A. Per-gunnar-martinsson, and . Tropp, Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, SIAM review, vol.53, issue.2, pp.217-288, 2011.

R. Horisaki, R. Takagi, and J. Tanida, Learning-based imaging through scattering media, Optics express, vol.24, issue.13, pp.13738-13743, 2016.

K. Jaganathan, Y. C. Eldar, and B. Hassibi, Phase retrieval: An overview of recent developments, 2015.

W. Kim, H. Monson, and . Hayes, Phase retrieval using two fourier-transform intensities, JOSA A, vol.7, issue.3, pp.441-449, 1990.

Q. Le, T. Sarlós, and A. Smola, Fastfood-approximating kernel expansions in loglinear time, Proceedings of the international conference on machine learning, vol.85, 2013.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz et al., Imaging with nature: Compressive imaging using a multiply scattering medium, Scientific reports, vol.4, p.5552, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01025647

P. Netrapalli, P. Jain, and S. Sanghavi, Phase retrieval using alternating minimization, Advances in Neural Information Processing Systems, pp.2796-2804, 2013.

A. Rahimi and B. Recht, Random features for large-scale kernel machines, Advances in Neural Information Processing Systems, pp.1177-1184, 2008.

A. Saade, F. Caltagirone, I. Carron, L. Daudet, A. Drémeau et al., Random projections through multiple optical scattering: Approximating kernels at the speed of light, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6215-6219, 2016.

G. Satat, M. Tancik, O. Gupta, B. Heshmat, and R. Raskar, Object classification through scattering media with deep learning on time resolved measurement, Optics express, vol.25, issue.15, pp.17466-17479, 2017.

P. Hans-schoenemann, A solution of the orthogonal Procrustes problem with applications to orthogonal and oblique rotation, 1964.

M. Sharma, A. Christopher, S. Metzler, O. Nagesh, . Cossairt et al., Inverse scattering via transmission matrices: Broadband illumination and fast phase retrieval algorithms, IEEE Transactions on Computational Imaging, 2019.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao et al., Phase retrieval with application to optical imaging: a contemporary overview, IEEE signal processing magazine, vol.32, issue.3, pp.87-109, 2015.

P. Stoica and J. Li, Lecture notes-source localization from range-difference measurements, IEEE Signal Processing Magazine, vol.23, issue.6, pp.63-66, 2006.

. Warren-s-torgerson, Multidimensional scaling: I. theory and method, Psychometrika, vol.17, issue.4, pp.401-419, 1952.

A. Joel, A. Tropp, M. Yurtsever, V. Udell, and . Cevher, Practical sketching algorithms for low-rank matrix approximation, SIAM Journal on Matrix Analysis and Applications, vol.38, issue.4, pp.1454-1485, 2017.

Y. Yang, M. Pilanci, J. Martin, and . Wainwright, Randomized sketches for kernels: Fast and optimal nonparametric regression, The Annals of Statistics, vol.45, issue.3, pp.991-1023, 2017.

F. Xinnan, X. Yu, A. Theertha-suresh, M. Krzysztof, D. N. Choromanski et al., Orthogonal random features, Advances in Neural Information Processing Systems, pp.1975-1983, 2016.

A. Yurtsever, M. Udell, J. A. Tropp, and V. Cevher, Sketchy decisions: Convex low-rank matrix optimization with optimal storage, 2017.

H. Zhang, Y. Liu, and H. Lei, Localization from incomplete euclidean distance matrix: Performance analysis for the svd-mds approach, IEEE Transactions on Signal Processing, 2019.